code: def _build_device(self, opt):
      seed = opt["seed"]
      np.random.seed(seed)
      if opt["use_gpu"] != 0 and torch.cuda.is_available():
          print("[ Using CUDA ]")
          torch.manual_seed(seed)
          torch.cuda.manual_seed_all(seed)
          from torch.backends import cudnn
  
          cudnn.benchmark = True
          device = torch.device(
              "cuda" if opt["gpu"] < 0 else "cuda:%d" % opt["gpu"]
          )
      else:
          print("[ Using CPU ]")
          device = torch.device("cpu")
      self.device = device
  
imports: import os
  import resource
  import numpy
  import torch
  import torch.optim
  from torch.utils.data import DataLoader
  from torch.utils.tensorboard import SummaryWriter
  from graph4nlp.pytorch.models.graph2seq import Graph2Seq
  from graph4nlp.pytorch.models.graph2seq_loss import Graph2SeqLoss
  from graph4nlp.pytorch.modules.evaluation import BLEU
  from args import get_args
  from build_model import get_model
  from dataset import IWSLT14Dataset
  from utils import WarmupCosineSchedule, get_log, wordid2str
label_desc: Use image node_id 2 for calling the NMT obj's underlying member method code with example usage: obj._build_device(opt) without return types